The assessment of body composition, particularly fat and fat free mass, is vital to understanding many health-related conditions including obesity and sarcopenia, whose very definitions depend on assessment of fat and fat free mass, but also cachexia induced by HIV, cancer, and other diseases; multiple sclerosis; wasting in neurological disorders such as Parkinson's, Alzheimer's, muscular dystrophy; eating disorders; proper growth in children; and yet others still. Nevertheless, challenges remain in measuring body composition. Existing methods beyond height, weight, and very simple anthropometry are still not the norm in large-scale epidemiologic studies and clinical studies. This is because of concerns, depending on method, about cost, portability, time, radiation exposure, and accuracy. Calculation of body mass index (BMI; kg/m2) is a commonly used alternative, but is limited in that it is an assessment of body weight relative to height and not of body fatness per se. A hand-held device that could be carried by any individual, accurately assess fat mass and skeletal muscle mass, and be utilized without any discomfort to the subject, inexpensively, and without radiation exposure would be highly desirable. Evidence suggests that highly experienced trained observers can accurately estimate percentage of body fat by visual examination of subjects, but concerns about accuracy and subjectivity predominate. We hypothesize that we can develop a computer algorithm which can perform as or more accurately when analyzing photographic imagery data, will require no repeated retraining, and eliminate subjectivity. Our first specific aim is to develop a computer image analysis algorithm to accurately estimate adiposity and skeletal muscle mass from standardized photographic images in a bi-ethnic sample of men, women, and children. Our second specific aim is to validate the algorithm in a large diverse sample of men, women, and children.